A MULTINOMIAL NAÃVE BAYES DECISION SUPPORT SYSTEM FOR COVID-19 DETECTION
Coronavirus disease 2019 termed COVID-19 is a highly infectious and pathogenic illness caused by severe acute respiratory syndrome. Symptoms of COVID-19 range from mild to severe, in some cases leading to death. Early detection could help to monitor progression of the disease, mitigate spread of the disease and possibly reduce mortality rate. Computer-aided diagnosis systems are designed to complement health care systems and assist in the early detection of diseases. Currently, as it is not possible to test all citizens especially in developing countries with very large populations due to financial constraints and the standard of their healthcare facilities, the problem of identifying suspected cases and deciding laboratory test priority among citizens is evident and more pressing. Therefore in this study, we introduce an interactive Artificial Intelligent web system using the Multinomial NaÃ¯ve Bayes algorithm with the aim of detecting warning COVID-19 symptoms and to provide fitting suggestions. Furthermore, the study also evaluates the performance of the Multinomial NaÃ¯ve Bayes based on the different holdout approaches experimented. The experimental results are promising as the Multinomial NaÃ¯ve Bayes is shown to achieve high accuracy detection thus providing a reliable method to identify warning symptoms of COVID-19.
Freitas Barbosa, V., Gomes, J. C., Lima, C., Calado, R. B., Bertoldo JÃºnior, C. R., Albuquerque, J. A., . . . Santos, W. (2020). Covid-19 rapid test by combining a random forest based web system and blood tests. Brazil.
Hemdan, E.-D., Shouman, M. A., & Karar, M. E. (2020). COVIDX-Net: A Framework of Deep Learning Classifiers to Diagnose. arXiv preprint arXiv.
John Hopkins University and Medicine. (2020, July 10). Retrieved July 10, 2020, from https://coronavirus.jhu.edu/map.html.
Krishnaiah, V., Narsimha, D., & Chandra, D. S. (2013). Diagnosis of Lung Cancer Prediction System Using Data Mining Classification Techniques. International Journal of Computer Science and Information Technologies, 4(1), 39-45.
Mayo Clinic. (2020, July 03). Retrieved July 07, 2020, from https://www.mayoclinic.org/diseases-conditions/coronavirus/symptoms-causes/syc-20479963.
McCallum, A., & Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification. AAAI-98 workshop on learning for text categorization, 752(1), 41-48.
Shi, F., Wang, J., Shi, J., Wu, Z., Wang , Q., Tang, Z., . . . Shen, D. (2020). theReview of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering.
Vaishya, R., Javaid, M., Khan, I. H., & Haleem, A. (2020). Artificial Intelligence (AI) application for COVID-19 pandemic. Diabetes & Metabolic Syndrome: Clinical Research & Reviews, 14(4), 337-339.
Xu, K., Zhou, M., Yang, D., Ling, Y., Liu, K., Bai, T., . . . Cheng, Z. (2020). Application of ordinal logistic regression analysis to identify the determinants of illness severity of COVID-19 in China. Epideminology & Infection, 148, 1-25.
Ying, S., Zheng, S., Li, L., Zhang, X., Zhang, X., Huang, Z., . . . Yang, Y. (2020). Deep learning Enables Accurate Diagnosis of Novel Coronavirus (COVID-19) with CT images. China.
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